Databricks-DA-Assoc Importing Data Practice Question
When importing data into Databricks, what is the primary benefit of using Delta Lake over standard Parquet files?
⚠ Common exam trap
Candidates frequently confuse Delta Lake with simple storage formats like Parquet or Avro, overlooking that Delta's primary value proposition is the transaction log enabling ACID properties and concurrency.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Delta Lake supports ACID transactions and schema enforcement.
Delta Lake adds a transaction log (the _delta_log folder) to Parquet files, enabling ACID transactions and time travel. This prevents data corruption during concurrent writes and allows users to query previous versions of data. This is a foundational concept for data reliability, as it ensures that analytical queries are always executed against a consistent and version-controlled state, which is impossible with standard, non-transactional Parquet files in a distributed system.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Delta Lake files require less storage space than Parquet.
Why it's wrong here
Delta Lake uses Parquet as the underlying storage format. Therefore, the actual data on disk is essentially the same size as raw Parquet. The transaction log adds a small amount of extra metadata overhead, but the actual data compression ratio remains identical to standard Parquet files.
- ✓
Delta Lake supports ACID transactions and schema enforcement.
Why this is correct
Delta Lake provides ACID transactions, which allow multiple readers and writers to interact with the data without corruption. It also enforces schema constraints, ensuring that new data matches the table structure. This makes it far more robust than standard Parquet, which lacks these native management features for distributed data.
- ✗
Delta Lake is faster for reading large tables than Parquet.
Why it's wrong here
The performance of reading data is comparable because Delta uses the same underlying Parquet format. While Delta does provide metadata optimizations like file skipping, the raw read speed for a single file is determined by the Parquet format itself. The real advantage of Delta is reliability, not raw read speed.
- ✗
Delta Lake is natively supported by all external BI tools.
Why it's wrong here
Delta Lake is not natively supported by every single BI tool, although support is growing through Databricks SQL. Many BI tools still rely on standard connectors that work better with Parquet or other formats. The value of Delta lies in its internal integrity and management, not in universal tool compatibility.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-DA-Assoc practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-DA-Assoc exam.